Method and apparatus for channel feedback by multiple description coding in a wireless communication system

ABSTRACT

A communication system comprises evolved base nodes (eNBs) communicating via an over-the-air (OTA) link with low mobility user equipment (UE). A network can utilize the eNBs for cooperative beam shaping for interference nulling based upon a number of factors UE (e.g., coordinated multi-point (CoMP) optimization for feedback, quality of service (QoS), fairness, etc.). The UE advantageously transmits multiple description coding (MDC) that supports a determination by the eNBs that coherent channel conditions (e.g., frequency and/or time invariance) exists for combining feedback reports to realize reduced quantization error. In addition, the MDC feedback reports still support incoherent channel states in which each report can be used individually for interference nulling/beamforming. MDC can be performed with one codebook or a plurality of codebooks.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

The present application for patent claims is a divisional of U.S. patentapplication Ser. No. 12/348,827, filed Jan. 5, 2009, entitled “METHODAND APPARATUS FOR CHANNEL FEEDBACK BY MULTIPLE DESCRIPTION CODING IN AWIRELESS COMMUNICATION SYSTEM,” pending, which claim priority toProvisional Application No. 61/104,465 entitled “Method and Apparatusfor Channel Feedback in a Wireless Communication System” filed Oct. 10,2008, all of which are assigned to the assignee hereof and herebyexpressly incorporated by reference herein.

FIELD OF INVENTION

The exemplary and non-limiting aspects described herein relate generallyto wireless communications systems, methods, computer program productsand devices, and more specifically to techniques for improved channelquality feedback in a coordinated multi-point (CoMP) communicationnetwork.

BACKGROUND

Wireless communication systems are widely deployed to provide varioustypes of communication content such as voice, data, and so on. Thesesystems may be multiple-access systems capable of supportingcommunication with multiple users by sharing the available systemresources (e.g., bandwidth and transmit power). Examples of suchmultiple-access systems include code division multiple access (CDMA)systems, time division multiple access (TDMA) systems, frequencydivision multiple access (FDMA) systems, 3GPP Long Term Evolution (LTE)systems, and orthogonal frequency division multiple access (OFDMA)systems.

Generally, a wireless multiple-access communication system cansimultaneously support communication for multiple wireless terminals.Each terminal communicates with one or more base stations viatransmissions on the forward and reverse links. The forward link (ordownlink) refers to the communication link from the base stations to theterminals, and the reverse link (or uplink) refers to the communicationlink from the terminals to the base stations. This communication linkmay be established via a single-in-single-out, multiple-in-signal-out ora multiple-in-multiple-out (MIMO) system.

Universal Mobile Telecommunications System (UMTS) is one of thethird-generation (3G) cell phone technologies. UTRAN, short for UMTSTerrestrial Radio Access Network, is a collective term for the Node-B'sand Radio Network Controllers which make up the UMTS radio accessnetwork. This communications network can carry many traffic types fromreal-time Circuit Switched to IP based Packet Switched. The UTRAN allowsconnectivity between the UE (user equipment) and the core network. TheUTRAN contains the base stations, which are called Node Bs, and RadioNetwork Controllers (RNC). The RNC provides control functionalities forone or more Node Bs. A Node B and an RNC can be the same device,although typical implementations have a separate RNC located in acentral office serving multiple Node B's. Despite the fact that they donot have to be physically separated, there is a logical interfacebetween them known as the Iub. The RNC and its corresponding Node Bs arecalled the Radio Network Subsystem (RNS). There can be more than one RNSpresent in an UTRAN.

3GPP LTE (Long Term Evolution) is the name given to a project within theThird Generation Partnership Project (3GPP) to improve the UMTS mobilephone standard to cope with future requirements. Goals include improvingefficiency, lowering costs, improving services, making use of newspectrum opportunities, and better integration with other openstandards. The LTE system is described in the Evolved UTRA (EUTRA) andEvolved UTRAN (EUTRAN) series of specifications.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosed aspects. This summary isnot an extensive overview and is intended to neither identify key orcritical elements nor delineate the scope of such aspects. Its purposeis to present some concepts of the described features in a simplifiedform as a prelude to the more detailed description that is presentedlater.

In accordance with one or more aspects and corresponding disclosurethereof, various aspects are described in connection with providinghigher order spatial channel feedback from user equipment (UE) to basenodes so that improved interference nulling can be achieved throughbeamforming. In particular, as cooperative transmissions are exploited,such as coordinated multi-point (CoMP) communications, UEs cansignificantly contribute to system performance improvement by providingsuch feedback.

In one aspect, a method is provided for wirelessly receiving feedback byreceiving a plurality of feedback reports, decoding multiple descriptioncoding of the feedback reports over a plurality of transmissionintervals, determining a coherent channel across the plurality oftransmission intervals, and combining a plurality of feedback reportsfor increased feedback accuracy for a coherent channel acrosstransmission intervals.

In yet an additional aspect, a computer program product is provided forwirelessly receiving feedback. A computer-readable storage mediumcomprises a first set of codes for causing a computer to receive aplurality of feedback reports. A second set of codes causes the computerto decode multiple description coding of the feedback reports over aplurality of transmission intervals. A third set of codes causes thecomputer to determine a coherent channel across the plurality oftransmission intervals. A fourth set of codes causes the computer tocombine a plurality of feedback reports for increased feedback accuracyfor a coherent channel across transmission intervals.

In yet another additional aspect, an apparatus is provided forwirelessly receiving feedback. Means are provided for receiving aplurality of feedback reports. Means are provided for decoding multipledescription coding of the feedback reports over a plurality oftransmission intervals. Means are provided for determining a coherentchannel across the plurality of transmission intervals. Means areprovided for combining a plurality of feedback reports for increasedfeedback accuracy for a coherent channel across transmission intervals.

In yet a further aspect, an apparatus is provided for wirelesslyreceiving feedback. A receiver receives a plurality of feedback reports.A computing platform determines a coherent channel across the pluralityof transmission intervals and decodes multiple description coding of thefeedback reports over a plurality of transmission intervals. Thecomputing platform combines a plurality of feedback reports forincreased feedback accuracy for a coherent channel across transmissionintervals.

To the accomplishment of the foregoing and related ends, one or moreaspects comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative aspectsand are indicative of but a few of the various ways in which theprinciples of the aspects may be employed. Other advantages and novelfeatures will become apparent from the following detailed descriptionwhen considered in conjunction with the drawings and the disclosedaspects are intended to include all such aspects and their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout and wherein:

FIG. 1 depicts a block diagram of a communication system of a networkutilizing a plurality of evolved Base Nodes (eNBs) for coordinatedmulti-point (CoMP) communication to a low mobility user equipment (UE)that transmits adaptive data rate and payload feedback of channel statefor increased downlink interference nulling.

FIG. 2 depicts a timing diagram of a methodology for high order spatialchannel feedback.

FIG. 3 depicts a flow diagram of a methodology for adaptive feedbackrate and payload.

FIG. 4 depicts a flow diagram of an alternative methodology for adaptivefeedback rate and payload based upon multi-level coding (MLC).

FIG. 5 depicts a flow diagram of another alternative methodology foradaptive feedback rate and payload based upon multiple descriptioncoding (MDC) using multiple codebooks.

FIG. 6 depicts a flow diagram of an additional alternative methodologyfor MDC based upon a fixed codebook.

FIG. 7 depicts a flow diagram of a further alternative methodology foradaptive feedback rate and payload for both time and frequency varyingcodebooks.

FIG. 8 depicts a multiple access wireless communication system accordingto one aspect.

FIG. 9 depicts a block diagram of a communication system.

FIG. 10 depicts a block diagram of a system for generating andprocessing channel information feedback in a wireless communicationsystem.

FIG. 11 depicts a flow diagram of a methodology for coding andcommunicating channel feedback.

FIG. 12 depicts a block diagram of a computing platform of UE thatsupports means for performing adaptive feedback rate and payload.

FIG. 13 depicts a block diagram of a computing platform of a base nodethat supports means for receiving adaptive feedback rate and payload.

FIG. 14 depicts a plot of a simulation that each additional bit ofchannel state feedback improves interference suppression based onchannel coherence.

FIG. 15 depicts a plot of a simulation for moderate scheduling delay anda reasonable (L1) channel direction information (CDI) payload.

FIG. 16 depicts a plot of a simulation for averaging observations on astatic channel.

DETAILED DESCRIPTION

In an illustrative context for the present innovation, spatial feedbackfrom user equipment (UE) can be used to reflect channel directioninformation (CDI) or precoding matrix index (PMI) of various nodes orgenerally feedback that is quantized. Advantageously, high-rate feedbackcan be used to provide spatial processing gains for low mobility users.Accordingly to analysis, gains of spatial cooperation for pedestrian UEs(e.g., 1-3 km/h) can be realized. Such gains cannot be made relying uponmessage-based (L3) feedback. In a particular exemplary use, there is aneed to address spatial codebook design with higher accuracyrequirements (e.g., CoMP) compared to the case of multiple inputmultiple output (MIMO) precoding. Traditional approach to precodingfeedback design does not scale well as accuracy requirements increase.By contrast, feedback delivery and scheduling delays limit performancefor a high mobility UE (e.g., greater than 10 km/h velocity); thus gainsthrough spatial processing are limited for low mobility UEs regardlessof feedback accuracy. In particular, for low mobility UEs, channelcoherence across subsequent CDI or PMI reports can be exploited.

Various aspects are now described with reference to the drawings. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofone or more aspects. It may be evident, however, that the variousaspects may be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate describing these aspects.

As used in this application, the terms “component”, “module”, “system”,and the like are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, a processor, an object, anexecutable, a thread of execution, a program, and/or a computer. By wayof illustration, both an application running on a server and the servercan be a component. One or more components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. Any aspect or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs.

Furthermore, the one or more versions may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedaspects. The term “article of manufacture” (or alternatively, “computerprogram product”) as used herein is intended to encompass a computerprogram accessible from any computer-readable device, carrier, or media.For example, computer readable media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical disks (e.g., compact disk (CD), digital versatile disk(DVD) . . . ), smart cards, and flash memory devices (e.g., card,stick). Additionally it should be appreciated that a carrier wave can beemployed to carry computer-readable electronic data such as those usedin transmitting and receiving electronic mail or in accessing a networksuch as the Internet or a local area network (LAN). Of course, thoseskilled in the art will recognize many modifications may be made to thisconfiguration without departing from the scope of the disclosed aspects.

Various aspects will be presented in terms of systems that may include anumber of components, modules, and the like. It is to be understood andappreciated that the various systems may include additional components,modules, etc. and/or may not include all of the components, modules,etc. discussed in connection with the figures. A combination of theseapproaches may also be used. The various aspects disclosed herein can beperformed on electrical devices including devices that utilize touchscreen display technologies and/or mouse-and-keyboard type interfaces.Examples of such devices include computers (desktop and mobile), smartphones, personal digital assistants (PDAs), and other electronic devicesboth wired and wireless.

Referring initially to FIG. 1, a communication system 100 of a basestation, depicted as an evolved base node (eNB) 102, communicates via anover-the-air (OTA) link 104 with user equipment (UE), depicted as a lowmobility (e.g., pedestrian-carried) UE 106 and a higher mobility (e.g.,vehicle-carried) UE 108. The higher mobility UE 108 can be engaged in aconventional multiple input multiple output (MIMO) communication sessiondepicted at 110 with the eNB 102 as well as another session 112 with aneNB 114. In order that the eNBs 102, 114 can effectively perform beamshaping depicted respectively at 116, 118 to minimize interference toanother UE 106, the UE 108 transmits channel feedback 120. Due to rapidchange in location of the higher mobility UE 108, this feedback 120 canhave a minimal delay. Thus, in order to not consume OTA resources andprocessing capability, each spatial feedback transmission isadvantageously small in resolution.

By contrast, the low mobility UE 106 has an opportunity advantageouslyto transmit higher order spatial channel feedback in order that betterinterference nulling can be achieved. It should be appreciated that UE106 can be capable of performing legacy MIMO transmission such asdescribed for UE 108. In addition, the UE 106 can be responsive to itsdegree of mobility to enter into a higher order spatial channel feedbackmode when appropriate.

For coherent interference that varies little over a particular period oftime or over a particular portion of frequency spectra being used (e.g.,transmission intervals of time or frequency respectively), the lowmobility UE 106 can advantageously send a higher resolution feedbackmessage 122 for the channel state. In order to not adversely impact theOTA capacity, advantageously this feedback 122 can be transmitted infeedback A and feedback B portions 124, 126, perhaps even at a slowertransmission rate as depicted at 128 than the higher mobility UE 108.These portions of available channel state feedback are combined forenhanced interference nulling by a channel spatial feedback combiner 130of the eNB 102 for performing enhanced interference nulling depicted asbeam shaping 132.

In order to break up this feedback into smaller portions, in an aspectdiscussed in the aforementioned co-pending application, the UE 106 canadvantageously use one or more of a high order channel feedbackcomponent 140. In particular, Multiple Level Coding (MLC) component 142provides for sending a base feedback layer with subsequent enhancementlayers. Alternatively or in addition, rather than using the bestmatching code from a single codebook, a group of N best codes can becyclically or randomly selected as depicted at 144 so that a situationis avoided where the same low resolution code is repeatedly sent.Alternatively or in addition, multiple codebooks can be usedsequentially in order to provide different information, enabling the eNB102 to work with each code individually or to build up a higher orderchannel state understanding by combining the codes.

Thereby, the low mobility UE 106 can engage more effectively in forms ofcommunication that can benefit from enhanced interference nulling, suchas network MIMO as depicted at 150 where feedback A, B 152 is alsotransmitted to eNB 110, which responds in turn with beam shaping asdepicted at 154. In an exemplary use, multiple description coding andchannel state feedback in network MIMO are used by UE 106 with a network160 enabling coordination between eNB 102, 114.

Advantageously, in using an exemplary MDC approach at both UEs 106, 108in which a different code is selected based upon a transmissioninterval, the UEs 106, 108 need not necessarily change feedbacktransmission rate or inform the eNBs 102, 110 of such a change. Instead,the eNBs 102, 110 can determine that a coherent channel across thetransmission interval is indicated by the multiple feedback reports,providing an opportunity for combining feedback reports for a moreaccurate representation of the channel feedback with less quantizationerror. Alternatively or in addition, the UEs 106, 108 can adjust theirtransmission interval based upon a determined degree of mobility asdepicted.

Downlink coordinated multi-point (CoMP) framework implies cooperativetransmission from multiple network nodes (access points, cells or eNBs)to user equipment (UE) or multiple UEs so that inter-node interferenceis minimized and/or channel gain from multiple nodes is combined at UEreceiver. Such cooperative gain and particularly cooperativeinterference nulling rely upon availability of accurate channel stateinformation at the transmitter (CSIT) of every cooperating node. CSITfeedback is provided in the existing WWAN (Wireless Wide Area Network)air interfaces (e.g. UMB, LTE, WiMax) in a form of precoding direction.Specifically a codebook of precoding vectors (in the case of singlespatial stream transmission) and precoding matrices (in the case ofsingle or multi-user MIMO transmission) is used. Each element of thecodebook (vector or matrix) corresponds to the ‘best’ beam (set ofbeams) corresponding to the downlink channel and UE feeds back index ofthe best entry based on downlink channel measurements. Typically,precoding feedback is limited to 4-6 bits which is adequate for theexisting WWAN systems wherein feedback is optimized for single user(possibly MIMO) transmission and potentially intra-node space divisionmultiple access (SDMA). However, such a design proves to be insufficientin the context of inter-node cooperation for the following reasons.

In FIG. 2, in one aspect a methodology 200 is provided for high orderspatial channel feedback from a UE 202 that is used by a plurality ofeNBs 204, 206 used by a network 208 for cooperative transmissions (e.g.,CoMP). In block 210, UE 202 determines feedback channel state ratherthan preferred beam direction since the network can be in a betterposition to determine optimal beam shaping as well as reducing acomputational burden on the UE 202. The UE quantizes the feedback (block212). In an illustrative aspect, the network 208 uses the eNBs 204, 206for cooperative CoMP communication (block 214). CoMP efficiency relieson the ability of a group of cooperating nodes (cluster) to adaptivelychoose a set of UEs to be cooperatively served on a given time-frequencyresource based on channel conditions of these UEs as well as long orshort term fairness criterion, QoS etc. Note that the appropriate choiceof beams depends on the set of UEs served. Since a UE does not haveinformation about conditions/requirements of other UEs, a UE cannotdetermine the right set of beam vectors. Meanwhile as depicted at 216,218, UEs can feed back a (quantized version of) the channel measurementto various cooperating nodes 204, 206 so that appropriate beam vectorscan be computed at the network side based on UE feedback as well asother considerations (e.g. fairness, QoS, etc) (block 220). Hence, inthe context of CoMP, feeding back a (quantized) channel rather than theproposed beam direction seems to be more appropriate. Similarly to theexisting feedback scheme, vector (matrix) quantization can be used sothat UE feeds back index of a vector (matrix) from a pre-definedcodebook that matches channel measurement best (block 222). Note thatsuch a feedback may be in the form of the actual complex channel frommultiple transmit antennas of one or more nodes to one or multiplereceive antennas of the UE (block 224); it can also be in a form wheree.g. channel normalized to a fixed norm and phase of any element of itsvector (so-called ‘channel direction’), principal eigenvector of thechannel matrix in the case of multiple receive antennas, etc. (block226)

In quantizing channel state information, the UE advantageously makes useof channel coherence to improve feedback accuracy at the network side(block 228). CoMP requires higher feedback accuracy compared to thesingle user beamforming/MIMO which are the main objectives of theexisting WWAN designs. Specifically, feedback needs to be accurateenough to enable interference nulling by a cooperative node or set ofnodes. A simple analysis shows that e.g. 6-bit feedback design in asystem with 4 transmit antennas and Gaussian IID (independent andidentically distributed) channels yields average interference nullinggain on the order of 6 dB only and every additional bit improves theachievable interference nulling by about 1 dB. Hence a design that aimsat 10 dB nulling gain would require 10 bits per feedback report which isroughly double of what is currently used. Unlike the existing systemswhere only feedback relative to a single serving node is needed, in CoMPfeedback of the channels between the UE and all cooperative nodes isneeded. This fact further drives the overall feedback rate requirements.

It is, however, important to note that high interference nulling gainscan be achieved only for UEs with relatively low mobility. Indeed,medium to high UE mobility on one hand and delay between the time ofchannel measurement and feedback calculation at UE and the actualdownlink transmission by cooperating nodes (referred to as schedulingdelay) on the other hand limit achievable nulling gain. This is depictedas determination by the UE at block 230 and recognition of the network208 at block 232 that a high order channel state feedback is being used.A high resolution (low quantization error) feedback from medium-highmobility UEs is therefore less valuable since channel variations causedby scheduling delay limit nulling gains. The key observation here isthat high channel feedback accuracy is needed for UEs with relativelylow mobility only. Hence it is natural to think of exploiting channelcoherence across time to improve feedback accuracy for a givenresolution (number of bits per report) on the UE feedback. In thefollowing sections, we highlight a few techniques that achieve thisgoal.

In FIG. 3, a methodology 300 is depicted that advantageously usesadaptive report rate and payload, leveraging an observation that thatlow mobility UEs do not need to feed back channel state as often as highmobility UEs one hand, depicted as an initial state in block 302, butlow mobility UEs warrant higher feedback accuracy (i.e., to make surethat the latter is not the limiting factor for the achievable nullinggain) on the other hand. Hence a simple approach would consist ofslowing down feedback and increasing payload for lower mobility UEs.Thus, in block 304, the UE determines that it is in a low mobilitystate. In response, the UE reduces feedback rate and increases feedbackpayload (block 306). As a specific example, assume that uplink overheadconsiderations yield 4-bit feedback every 8 ms, which is equivalent to a0.5 kbps feedback channel. In this case, one could consider a designwhere UEs with high(er) mobility feedback a 4-bit report every 8 mswhile low(er) mobility UEs feedback 8-bit report every 16 ms. Theobvious drawbacks of this approach are (a) a need to tune report formatto UE mobility which implies either explicit communication (handshake)between the UE and the network or additional bits to indicate the format(block 308) as well as impacts due to (b) increased feedback delay (8 msto 16 ms) that limits nulling gains.

Alternatively, the network can recognize the low mobility of the UE andcan anticipate receiving high order feedback without additionalhandshaking or signaling. As a further alternative, in someimplementations the UE can increase feedback accuracy in a low mobilitystate without necessarily reducing the feedback rate in proportion or atall.

In FIG. 4, an alternative approach to achieving high order feedback(e.g., reduced quantization errors) is depicted as a methodology 400 forMulti-Level Coding (MLC). Multi-level coding principle is widely used insource coding, namely speech, audio and video coding. The basic idea ofmulti-level coding is to exploit channel correlation to achieve the mostaccurate representation for a given payload size (block 402).Practically speaking, multi-level coding implies periodic infrequentfeedback of the full quantized channel (usually called base layer)(block 404) and more frequent feedback of ‘differential channel’ or‘innovation’ (block 406). The higher channel correlation across time thebetter feedback accuracy can be achieved with a given number of bits.Multi-level coding arguably provides the best accuracy for a givenpayload size. There is however a number of drawbacks to this approach.First of all, base layer needs to be sent with higher reliability thanenhancement layer as the loss of base layer means loss of feedback untilbase layer is retransmitted (block 408). This also implies that baselayer should be transmitted fairly often and/or explicit signaling isneeded from the network to UE to request retransmission of the baselayer (block 410). Second, multi-level coding gain depends on theaccuracy of process modeling (i.e. approximation of channel correlationacross time). Hence process parameters need to be periodically estimatedand agreed upon between the UE and the network, depicted at 412.Finally, implementing multi-level coding implies multiple changes to thefeedback structure at PHY/MAC level as well as additional signaling tocommunicate feedback format, (correlation) parameters etc.

In FIG. 5, an exemplary alternative for increased feedback accuracy isdepicted as a methodology 500 for Multiple Description Coding (MDC). Thegeneral idea of multiple description coding consists of using multiplecode descriptions to improve the accuracy of the source representationat the receiver. In the present context of channel state feedback, thiscould be implemented by e.g. using different codebooks with the samestatistical properties at different time instances (block 502). Toillustrate this concept, let's assume a static or low mobility(time-invariant) channel (block 504). In the existing WWAN systems,constant (time invariant) codebook is used hence UE would feed exactlythe same precoding index to the network at every time instance assumingaccurate channel state estimation at the receiver. Hence multipleconsecutive feedback reports do not provide and additional informationand channel state estimation at the network is defined by quantizationaccuracy (payload) size of a single feedback instance. Now assume that atime varying codebook is used. In the latter case, every instance ofchannel feedback refers to an entry from a different codebook henceyielding a different precoding matrix or vector (block 506). Instead ofobtaining exactly the same information regarding channel state ondifferent reports (as in fixed codebook case), the network getsdifferent ‘looks’ at the channel state (block 508). Based on the networkassessment of low UE mobility (block 510), the network may choose tosuitably combine these reports to improve the accuracy of channel stateas compared to a single report from a fixed codebook (block 511).

Various specific ways of how to combine multiple reports may beconsidered depending on the type of channel state feedback. The two‘typical’ examples are full channel feedback (block 512) andEigen-direction feedback (block 514). In the former case, optimalcombining may be achieved through linear (e.g., minimum means squarederror (MMSE)) filtering of channel state feedback corresponding todifferent instances with the proper choice of filter parametersconsistent with UE mobility, assuming that channel state is a Gaussianprocess (block 516). In the case of Eigen-direction feedback, optimalsolution is not straightforward but some heuristics can be used (block518). For instance, channel state estimate can be obtained as theprincipal component of a non-negative Hermitian matrix computed as aweighted sum of outer auto-products of channel states received atdifferent instances with the proper choice of weighting profileconsistent with UE mobility (block 520). Alternatively, the combinationcan be performed by MMSE (block 521).

Now let us consider a mobile UE such that channel state is fullyuncorrelated between the adjacent reports (block 522). While the sametime-varying codebook can be used, the network will use the most recentreport from the UE to estimate channel state (block 524). Since everycodebook in the time varying sequence has the same statisticalproperties as a fixed codebook, channel state feedback accuracy with atime-varying codebooks will be no different compared to the case of afixed codebook. Hence the same sequence of codebooks can be usedregardless of UE mobility.

Based on the above facts and further obvious observations, we cansummarize some useful properties of multiple description coding (MDC)based on time-varying codebook design:

First, time varying codebook does not depend on UE mobility and channelvariation statistics. Hence a single sequence of codebooks can be usedto replace a single codebook. The length of this sequence is defined bythe maximum number of reports considered for combining. As increase incombining gain reduces along with the number of combined reports even atrelatively low mobility, practical sequence length can be limited tosingle digit or low double digit numbers. Once sequence length is fixed,this sequence can be re-used (e.g., in a round robin fashion).

Second, for a ‘sensible’ UE implementation, no additional complexity atthe UE is incurred due to time varying codebook compared to a fixed one.Indeed, UE computes precoding feedback for a given codebook based on thebest match of channel estimate across all entries of the codebook. Inthe case of time invariant codebook, matching is performed with respectto the same codebook while in the case of time varying codebook matchingwith respect to different codebooks is performed at different timeinstances. Note that memory requirements will not be large if timevarying sequence has limited size while performance gain even with 2-4codebooks turns to be substantial. Also note that fairly good codebookscan be generated in a pseudo-random fashion (based on a pre-determinedlow-complexity algorithm seeded by a number) in which case there wouldbe no additional memory requirements and there is no need/reason tolimit sequence length to a small number.

Third, combining of multiple reports is optional at the network. Asexplained before in the case of time varying channel, the network canuse the most recent report only from the UE thereby achieving the samefeedback accuracy as the standard fixed codebook design. A ‘lazy’network implementation would use the most recent report regardless of UEmobility and hence will achieve performance/complexity tradeoff that isachievable with a fixed codebook. Meanwhile, a ‘smart’ networkimplementation could combine multiple reports based on UE mobilityestimated at the network.

Fourth, every instance of feedback can have the same format andreliability requirements yielding a homogeneous control signaling(PHY/MAC) design. In the context of 3GPP LTE evolution (LTE-Advanced),it may be possible to reuse the existing UE feedback format defined inLTE Rel-8 based on PUCCH. Furthermore, as already mentioned, codebookstructure does not depend on UE mobility hence there is no need foradditional periodic signaling between the UE and the network to synch-upon feedback format and parameters (unlike in the case of adaptive reportrate/payload or multi-level coding).

Note that multiple description coding principle can be applied in thecase of a fixed codebook as well. Instead of feeding back the index ofthe best matching precoding entry, a low mobility UE could feed back ina round-robin fashion its N best entries. Performance gain of thissolution remains non-negligible for low-mobility UEs so long N isrelatively small although it is certainly less than performance gainobtained when the best precoding entry is reported at every instance anda time varying codebook is used. Another clear drawback of this approachis that N needs to be updated according to UE channel conditions sinceusing N>1 is clearly detrimental for high mobility UEs where nocombining is possible. Updating N means additional signaling between theUE and the network which is also undesirable.

Thus, in FIG. 6, as a further alternative for increased feedbackaccuracy, a methodology 600 is depicted wherein a single codebook isused. In particular, it is recognized that the best representation fromthe codebook can be repetitively sent if the channel state is coherent.Thus, the network gains no additional insight into the channel state bysuch repetitively sent feedback. For a codebook of sufficient size, aplurality of N representations can be selected as a group that are the“best” and randomly or cyclically sent such that the network can combinethese representations to realize a more accurate representation of thechannel state. To that end, in block 602, the UE measures the channelstate. A codebook is accessed (block 604) and a plurality of Nrepresentation codes are selected as most closely approximating thechannel state (block 606). One of the codes is selected (e.g., rankordered as best, randomly selected, sequentially selected) (block 608) Adetermination is made as to whether this was the code sent during thelate feedback transmission (block 610). If so, processing returns toblock 608 to select another code. If appropriate in block 610, then theselected code is sent (block 612). At the network, the series of codesare recognized as being directed to an essentially coherent channel,enabling a combination of the code representations to achieve a moreaccurate representation (block 614). Based upon this determination,improved interference nulling can be performed by appropriatecooperative beamforming (block 616).

Returning to observations regarding multiple description coding, fifth,the described multiple description coding principle can be applied inthe context of frequency coherence as well. In FIG. 7, a methodology 700is depicted for taking advantage of frequency coherence for higheraccuracy in channel state feedback. By using frequency varying codebook(e.g. different codebooks corresponding to different sub bands) (block702), we can improve channel state accuracy for channels with a goodcoherence in the frequency domain, hence moderate frequency selectivity.In a more general setting, one should consider time and frequencyvarying codebook design where channel state quantization within a giventime instance (e.g. sub-frame) and a given sub band is performedaccording to a codebook associated with this slot/sub band pair (block704). The network can further combine reports corresponding to differenttime instances and sub bands based on the estimated time/frequencycoherence of the underlying channel (block 706).

Generally, a wireless multiple-access communication system cansimultaneously support communication for multiple wireless terminals.Each terminal communicates with one or more base stations viatransmissions on the forward and reverse links. The forward link (ordownlink) refers to the communication link from the base stations to theterminals, and the reverse link (or uplink) refers to the communicationlink from the terminals to the base stations. This communication linkmay be established via a single-in-single-out, multiple-in-signal-out ora multiple-in-multiple-out (MIMO) system.

A MIMO system employs multiple (N_(T)) transmit antennas and multiple(N_(R)) receive antennas for data transmission. A MIMO channel formed bythe N_(T) transmit and N_(R) receive antennas may be decomposed intoN_(S) independent channels, which are also referred to as spatialchannels, where N_(S)≦min {N_(T), N_(R)}. Each of the N_(S) independentchannels corresponds to a dimension. The MIMO system can provideimproved performance (e.g., higher throughput and/or greaterreliability) if the additional dimensionalities created by the multipletransmit and receive antennas are utilized.

A MIMO system supports a time division duplex (TDD) and frequencydivision duplex (FDD) systems. In a TDD system, the forward and reverselink transmissions are on the same frequency region so that thereciprocity principle allows the estimation of the forward link channelfrom the reverse link channel. This enables the access point to extracttransmit beamforming gain on the forward link when multiple antennas areavailable at the access point.

Referring to FIG. 8, a multiple access wireless communication systemaccording to one aspect is illustrated. An access point 850 (AP)includes multiple antenna groups, one including 854 and 856, anotherincluding 858 and 860, and an additional including 862 and 864. In FIG.8, only two antennas are shown for each antenna group, however, more orfewer antennas may be utilized for each antenna group. Access terminal(AT) 866 is in communication with antennas 862 and 864, where antennas862 and 864 transmit information to access terminal 866 over forwardlink 870 and receive information from access terminal 866 over reverselink 868. Access terminal 872 is in communication with antennas 856 and858, where antennas 856 and 858 transmit information to access terminal872 over forward link 876 and receive information from access terminal872 over reverse link 874. In a FDD system, communication links 868,870, 874 and 876 may use different frequency for communication. Forexample, forward link 870 may use a different frequency then that usedby reverse link 868. Each group of antennas and/or the area in whichthey are designed to communicate is often referred to as a sector of theaccess point 850. In the aspect, antenna groups each are designed tocommunicate to access terminals 866, 872 in a sector of the areascovered by access point 850.

In communication over forward links 870 and 876, the transmittingantennas of access point 850 utilize beamforming in order to improve thesignal-to-noise ratio of forward links for the different accessterminals 866 and 874. Also, an access point using beamforming totransmit to access terminals scattered randomly through its coveragecauses less interference to access terminals in neighboring cells thanan access point transmitting through a single antenna to all its accessterminals.

An access point 850 may be a fixed station used for communicating withthe terminals and may also be referred to as an access point, a Node B,or some other terminology. An access terminal 866, 872 may also becalled user equipment (UE), a wireless communication device, terminal,access terminal or some other terminology.

FIG. 5 is a block diagram of an aspect of a transmitter system 910 (alsoknown as the access point) and a receiver system 950 (also known asaccess terminal) in a MIMO system 900. At the transmitter system 910,traffic data for a number of data streams is provided from a data source912 to a transmit (TX) data processor 914.

In an aspect, each data stream is transmitted over a respective transmitantenna. TX data processor 914 formats, codes, and interleaves thetraffic data for each data stream based on a particular coding schemeselected for that data stream to provide coded data.

The coded data for each data stream may be multiplexed with pilot datausing OFDM techniques. The pilot data is typically a known data patternthat is processed in a known manner and may be used at the receiversystem to estimate the channel response. The multiplexed pilot and codeddata for each data stream is then modulated (i.e., symbol mapped) basedon a particular modulation scheme (e.g., BPSK, QSPK, M-PSK, or M-QAM)selected for that data stream to provide modulation symbols. The datarate, coding, and modulation for each data stream may be determined byinstructions performed by processor 930.

The modulation symbols for all data streams are then provided to a TXMIMO processor 920, which may further process the modulation symbols(e.g., for OFDM). TX MIMO processor 920 then provides N_(T) modulationsymbol streams to N_(T) transmitters (TMTR) 922 a through 922 t. Incertain implementations, TX MIMO processor 920 applies beamformingweights to the symbols of the data streams and to the antenna from whichthe symbol is being transmitted.

Each transmitter 922 receives and processes a respective symbol streamto provide one or more analog signals, and further conditions (e.g.,amplifies, filters, and upconverts) the analog signals to provide amodulated signal suitable for transmission over the MIMO channel. N_(T)modulated signals from transmitters 922 a through 922 t are thentransmitted from N_(T) antennas 924 a through 924 t, respectively.

At receiver system 950, the transmitted modulated signals are receivedby N_(R) antennas 952 a through 952 r and the received signal from eachantenna 952 is provided to a respective receiver (RCVR) 954 a through954 r. Each receiver 954 conditions (e.g., filters, amplifies, anddownconverts) a respective received signal, digitizes the conditionedsignal to provide samples, and further processes the samples to providea corresponding “received” symbol stream.

An RX data processor 960 then receives and processes the N_(R) receivedsymbol streams from N_(R) receivers 954 based on a particular receiverprocessing technique to provide N_(T) “detected” symbol streams. The RXdata processor 960 then demodulates, deinterleaves, and decodes eachdetected symbol stream to recover the traffic data for the data stream.The processing by RX data processor 960 is complementary to thatperformed by TX MIMO processor 920 and TX data processor 914 attransmitter system 910.

A processor 970 periodically determines which pre-coding matrix to use(discussed below). Processor 970 formulates a reverse link messagecomprising a matrix index portion and a rank value portion.

The reverse link message may comprise various types of informationregarding the communication link and/or the received data stream. Thereverse link message is then processed by a TX data processor 938, whichalso receives traffic data for a number of data streams from a datasource 936, modulated by a modulator 980, conditioned by transmitters954 a through 954 r, and transmitted back to transmitter system 910.

At transmitter system 910, the modulated signals from receiver system950 are received by antennas 924, conditioned by receivers 922,demodulated by a demodulator 940, and processed by a RX data processor942 to extract the reserve link message transmitted by the receiversystem 950. Processor 930 then determines which pre-coding matrix to usefor determining the beamforming weights then processes the extractedmessage.

In an aspect, logical channels are classified into Control Channels andTraffic Channels. Logical Control Channels comprises Broadcast ControlChannel (BCCH), which is DL channel for broadcasting system controlinformation. Paging Control Channel (PCCH), which is DL channel thattransfers paging information. Multicast Control Channel (MCCH) which isPoint-to-multipoint DL channel used for transmitting MultimediaBroadcast and Multicast Service (MBMS) scheduling and controlinformation for one or several MTCHs. Generally, after establishing RRCconnection this channel is only used by UEs that receive MBMS (Note: oldMCCH+MSCH). Dedicated Control Channel (DCCH) is Point-to-pointbi-directional channel that transmits dedicated control information andused by UEs having an RRC connection. In aspect, Logical TrafficChannels comprises a Dedicated Traffic Channel (DTCH), which isPoint-to-point bi-directional channel, dedicated to one UE, for thetransfer of user information. In addition, a Multicast Traffic Channel(MTCH) for Point-to-multipoint DL channel for transmitting traffic data.

In an aspect, Transport Channels are classified into DL and UL. DLTransport Channels comprises a Broadcast Channel (BCH), Downlink SharedData Channel (DL-SDCH) and a Paging Channel (PCH), the PCH for supportof UE power saving (DRX cycle is indicated by the network to the UE),broadcasted over entire cell and mapped to PHY resources which can beused for other control/traffic channels. The UL Transport Channelscomprises a Random Access Channel (RACH), a Request Channel (REQCH), anUplink Shared Data Channel (UL-SDCH) and plurality of PHY channels. ThePHY channels comprise a set of DL channels and UL channels.

The DL PHY channels comprises: Common Pilot Channel (CPICH);Synchronization Channel (SCH); Common Control Channel (CCCH); Shared DLControl Channel (SDCCH); Multicast Control Channel (MCCH); Shared ULAssignment Channel (SUACH); Acknowledgement Channel (ACKCH); DL PhysicalShared Data Channel (DL-PSDCH); UL Power Control Channel (UPCCH); PagingIndicator Channel (PICH); Load Indicator Channel (LICH); The UL PHYChannels comprises: Physical Random Access Channel (PRACH); ChannelQuality Indicator Channel (CQICH); Acknowledgement Channel (ACKCH);Antenna Subset Indicator Channel (ASICH); Shared Request Channel(SREQCH); UL Physical Shared Data Channel (UL-PSDCH); Broadband PilotChannel (BPICH).

In an aspect, a channel structure is provided that preserves low PAR (atany given time, the channel is contiguous or uniformly spaced infrequency) properties of a single carrier waveform.

For the purposes of the present document, the following abbreviationsapply:

-   -   AIS Automatic Identification System    -   AM Acknowledged Mode    -   AMD Acknowledged Mode Data    -   ARQ Automatic Repeat Request    -   BCCH Broadcast Control CHannel    -   BCH Broadcast CHannel    -   C- Control-    -   CCCH Common Control CHannel    -   CCH Control CHannel    -   CCTrCH Coded Composite Transport Channel    -   CDI Channel Direction Information    -   CP Cyclic Prefix    -   CRC Cyclic Redundancy Check    -   CTCH Common Traffic CHannel    -   DCCH Dedicated Control CHannel    -   DCH Dedicated CHannel    -   DL DownLink    -   DSCH Downlink Shared CHannel    -   DTCH Dedicated Traffic CHannel    -   FACH Forward link Access CHannel    -   FDD Frequency Division Duplex    -   i.i.d. independent and identically distributed    -   L1 Layer 1 (physical layer)    -   L2 Layer 2 (data link layer)    -   L3 Layer 3 (network layer)    -   LI Length Indicator    -   LSB Least Significant Bit    -   MAC Medium Access Control    -   MBMS Multimedia Broadcast Multicast Service    -   MCCH MBMS point-to-multipoint Control CHannel    -   MIMO Multiple Input Multiple Output    -   MRW Move Receiving Window    -   MSB Most Significant Bit    -   MSCH MBMS point-to-multipoint Scheduling CHannel    -   MTCH MBMS point-to-multipoint Traffic CHannel    -   PCCH Paging Control CHannel    -   PCH Paging CHannel    -   PDU Protocol Data Unit    -   PHY PHYsical layer    -   PhyCH Physical CHannels    -   QoS Quality of Service    -   RACH Random Access CHannel    -   RLC Radio Link Control    -   RRC Radio Resource Control    -   SAP Service Access Point    -   SDU Service Data Unit    -   SHCCH SHared channel Control CHannel    -   SN Sequence Number    -   SUFI SUper FIeld    -   TCH Traffic CHannel    -   TDD Time Division Duplex    -   TFI Transport Format Indicator    -   TM Transparent Mode    -   TMD Transparent Mode Data    -   TTI Transmission Time Interval    -   U- User-    -   UE User Equipment    -   UL UpLink    -   UM Unacknowledged Mode    -   UMB Ultra Mobile Broadband    -   UMD Unacknowledged Mode Data    -   UMTS Universal Mobile Telecommunications System    -   UTRA UMTS Terrestrial Radio Access    -   UTRAN UMTS Terrestrial Radio Access Network    -   WWAN Wireless Wide Area Network

Turning to FIG. 10, a block diagram of a system 1000 for generating andprocessing channel information feedback in a wireless communicationsystem is illustrated. System 1000 can include one or more Node Bs 1010and one or more UEs 1030, which can communicate via respective antennas1012 and 1032. In one example, UE 1030 can provide spatial feedback toNode B 1010, which can be utilized by Node B 1010 to ascertain CDI ofvarious network nodes and perform spatial processing. Alternatively,precoding matrix index (PMI) can be the form of feedback. CDI representsthe actual channel (or a normalized channel) between the network (eNB)and the UE while PMI represents a precoder (beam) suggested by the UE tothe eNB based on channel measurements performed as the UE.

In one aspect, UE 1030 can include a feedback coding module 1038, whichcan generate and/or otherwise identify channel state information (e.g.,spatial feedback information) for transmission to Node B 1010. In oneexample, feedback coding module 1038 can utilize multiple descriptioncoding to encode spatial feedback information as described herein togenerate a series of streams for spatial feedback that can becommunicated from transceiver(s) 1034 and antenna(s) 1032 to Node B1010. For example, feedback coding module 1038 can utilize a set ofcodebooks 1036 to encode respective streams. In an aspect, codebooks1036 can be configured with substantially similar properties and can beconfigured to vary in time, frequency, and/or any other suitableinterval. Spatial feedback streams can then be received at Node B 1010via antenna(s) 1012 and transceiver(s) 1014. Upon receipt of the spatialfeedback at Node B 1010, a feedback decoding module 1016 and/or aspatial processor 1018 can be utilized to obtain a channel estimatecorresponding to UE 1030, based on which transmissions to UE 1030 can beadjusted. By utilizing multiple description coding in the describedmanner, it can be appreciated that the interference suppressionperformance of system 1000 can be increased without requiring anincrease in spatial codebook size.

In another aspect, multiple description coding as described herein canbe performed in a manner that is transparent to UE 1030 withoutrequiring changes in the processing and/or reporting rules utilized byUE 1030. In one example, feedback decoding module 1016 and/or spatialprocessor 1018 at Node B 1010 can extrapolate spatial feedbackinformation corresponding to a UE 1030 from past reports from the UE1030 in order to compress errors.

As FIG. 10 illustrates, Node B 1010 can additionally utilize a processor1022 and/or memory 1024 to implement the above functionality and/orother suitable functionality. Similarly, UE 1030 can include a processor1042 and/or a memory 1044 that can be utilized to implement the abovefunctionality and/or other suitable functionality.

FIG. 11 illustrates a methodology 1100 for coding and communicatingchannel feedback. At block 1102, spatial feedback information isidentified. At block 1104, the spatial feedback information is encodedvia multiple description coding using a series of codebookscorresponding to respective intervals in time and/or frequency. At block1106, the coded spatial feedback information is transmitted at theintervals corresponding to the codebooks.

In FIG. 12, user equipment (UE) 1200 has a computing platform 1202 thatprovides means such as sets of codes for causing a computer to performadaptive feedback rate and payload for more accurate channel stateinformation at the transmitter. In particular, the computing platform1202 includes a computer readable storage medium (e.g., memory) 1204that stores a plurality of modules 1206-1210 executed by a processor(s)1212, which also controls a transmitter/receiver component 1214 forcommunicating with eNBs (FIG. 13). In particular, a means (module) 1206is provided for transmitting channel state feedback to a network in amanner supportive of incoherent channel states. A means (module) 1208 isprovided for determining mobility of less than a threshold (i.e., a lowmobility state that warrants increasing feedback accuracy perhaps withreduced frequency of transmission). A means (module) 1210 is providedfor transmitting channel state feedback at a lengthened interval andwith reduced quantization error.

In FIG. 13, evolved base node (eNB) 1300 has a computing platform 1302that provides means such as sets of codes for causing a computer toreceive and use adaptive feedback rate and payload for more accuratechannel state information at the transmitter. In particular, thecomputing platform 1302 includes a computer readable storage medium(e.g., memory) 1304 that stores a plurality of modules 1306-1310executed by a processor(s) 1312, which also controls atransmitter/receiver component 1314 for communicating with UE (FIG. 12).In particular, a means (module) 1306 is provided for receiving channelstate feedback from user equipment (UE). A means (module) 1308 isprovided for determining a change in the channel state feedback rate andpayload from the UE that occurs when mobility of the UE is less than athreshold. A means (module) 1310 is provided for receiving channel statefeedback at a lengthened interval and with reduced quantization error.

With regard to codebook performance metric, assume frequency flatchannel between M_(TX) TX (transmit) antennas & one RX (receiving)antenna. Consider extensions to frequency selective channels via channelexpansion in a suitable basis, quantization of the expansioncoefficients and subsequent reconstruction. Suitable basis could bequantization of “flat” channels from different sub-bands, time domain(tap) quantization, etc. Codebook performance can be defined in terms ofthe distribution tail of the maximum correlation of a channel with thebest codeword. Thus, consider a codebook (C) defined

C = [C₁, …  , C_(N_(CW))]C_(l) = 1, where  1 ≤ i ≤ N_(CW)${r_{\alpha}^{2}(C)} = {\arg_{\mu}\left\{ {{{\mathbb{P}}\left\{ {{\begin{matrix}\max \\{1 \leq l \leq N_{CW}}\end{matrix}{{C_{l}^{*}h}}^{2}} \leq {\mu {h}^{2}}} \right\}} = \alpha} \right\}}$

where Probability

is with respect to distribution of the M_(rx)x1 channel h, C_(l) is theM_(rx)x1 codeword vector, the CDI payload (bits) is given byN_(CW)=2^(N) ^(B) . As a rationale, consider for a static channel h, abeamforming vector that is chosen orthogonal to the reported CDIguarantees interference suppression with probability not less than (1−α)of at least

−10 log₁₀(1−r _(α) ²(C))[dB]

Thus, regarding codebook performance analysis, numerical analysissuggests that as codebook size increases a codebook chosen randomlyconsistent with the distribution of h is as good as a codebook chosen tomaximize r_(α) ²(C).

Performance of a random codebook can be assessed analytically for afamily of complex circular Gaussian channels. Consider only the case ofi.i.d. (independent and identically distributed) channel h althoughextension to correlated channels is possible. Random codebook C isgenerated as a set of i.i.d. complex circular Gaussian channelsnormalized to a unit norm

${{\mathbb{P}}\left\{ {{\begin{matrix}\max \\{1 \leq l \leq N_{CW}}\end{matrix}{{C_{l}^{*}h}}^{2}} \leq {\mu {h}^{2}}} \right\}} = \left( {1 - \left( {1 - \mu} \right)^{M_{TX} - 1}} \right)^{N_{CW}}$${1 - {r_{\alpha}^{2}(C)}} = \left( {1 - \alpha^{2 - N_{B}}} \right)^{\frac{1}{M_{TX} - 1}}$

Thus, a theoretical relationship can be shown between interferencesuppression 1−r_(α) ²(C) and probability α.

Regarding, an empirical relationship based on optimized codebooks,select the best of 10³ randomly chosen codebooks, estimated r_(α) ²(C)based on 10⁴ random channels with results plotted as stars. Thereby, itcan be observed that every additional bit improves suppression by 1 dBbased on channel coherence as depicted at 1400 in FIG. 14.

Further, assume ideal quantization and consider suppression level causedby channel de-correlation due to mobility. Focusing on low mobility(pedestrian) UEs and assume first order interpolation, better resultsare achievable with a reasonably matched higher-order model. As depictedin FIG. 15 at 1500, for pedestrian UEs, moderate scheduling delay and areasonable (L1) CDI payload: channel feedback is a limiting factor,given velocity 1-3 km/h, scheduling delay ≦10 ms, CDI payload ≦12 bits.

As a high level approaches to the problem, one can tune reporting rateand payload size to channel variability. Update report format(resolution) can be adapted to UE mobility. In an exemplary approach,there can be a likely need to split payload over multiple reports due tohigher erasure rate.

One option for achieving higher resolution, multi-level coding (MLC) canbe used. MLC can require periodic reliable update of variation modelparameters. There can be different reliability requirements for base andenhancement layers of the MLC. For instance, it can be of highestimportance that a base layer is received in order to have meaningfulinformation whereas a subsequent enhancement layer can be missed withoutserious degradation in achieving interference nulling based upon spatialfeedback. One consideration for MLC is that higher complexity at UE canbe required to generate the MLC spatial channel feedback.

As an alternative, multiple description coding (MDC) can be used. Anexemplary implementation introduces multiple codebooks with the sameproperties for transmission interval (e.g., time-varying orfrequency-varying) codebooks. Advantageously, such MDC implementationdoes not impose a change in processing or reporting rules at UE.Further, a “lazy eNB” can use instantaneous reports without having tocombine MDC reports for a higher resolution spatial channel feedback,thus requiring no change with respect to a baseline. However, a “smart”eNB can extrapolate from past reports to compress errors.

As an illustrative analysis, consider a static channel case. Assumestatic flat channel between M_(TX) TX antennas and one 1 RX antenna.Assume that T different codebooks C⁽¹⁾, . . . , C^((T)) are used acrossT intervals and UE feeds back indices l₁, . . . , l_(N). A plausiblechannel estimate to use is given by the dominant principal component ofmatrix

$\sum\limits_{m = 1}^{T}{C_{l_{m}}^{(m)}C_{l_{m}}^{{(m)}*}}$

On a static channel, it can be observed that averaging over 2 (4)observations is equivalent to adding approximately 3 (5) bits, asdepicted in FIG. 16 at 1600.

With regard to analyzing time-selective channels, consider timevariations according to Jakes model with different speeds. Time varyingcodebooks are assumed. As an extension of the combined estimator, searchfor the principal component of a matrix

$\sum\limits_{m = 1}^{T}{{Q_{m}}^{2}C_{l_{m}}^{(m)}C_{l_{m}}^{{(m)}*}}$

where ρ_(m) is correlation between channels at the time of measurementand estimation: this weight proportional to the energy of the estimatedcomponent in the measured channel. Optimal solution is straightforwardif the entire CSI, including CDI as well as envelope, is quantized asgiven by Wiener minimum means squared error (MMSE) filter. In the caseof CDI only feedback, one can use the above mentioned ad-hoc solutiongiven by the principal component of a weighted sum of outer products ofthe recent CDI reports. Furthermore, a suitable adaptation of the Wiener(MMSE) solution to the CDI only feedback case is also possible.

Several observations can be made in summary. Accounting for channelcoherence across time is equivalent to a non-trivial increase infeedback payload size. The same applies to frequency domain variabilityas another form of a transmission interval. Potential solutions includeadapting payload size and reporting rate to UE mobility, which canrequire explicit dynamic signaling to change reporting format.Alternatively, multi-level coding can be used, which can requireexplicit signaling and parameter tuning with non-uniform errorprotection. As yet another alternative, a plurality of nearly optimalcodes can be used cyclically from the same codebook rather thancontinually sending the same code. As an exemplary alternative, multipledescription coding via time (or frequency) varying codebooks can beused, which is transparent to UE, achieves gains based on eNB algorithmwith no robustness issues as well as requiring minimal changes to AISand UE implementation.

It is understood that the specific order or hierarchy of steps in theprocesses disclosed is an example of exemplary approaches. Based upondesign preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged while remainingwithin the scope of the present disclosure. The accompanying methodclaims present elements of the various steps in a sample order, and arenot meant to be limited to the specific order or hierarchy presented.

It should be appreciated for clarity that the illustrative aspectsdescribed herein focus on UE encoding spatial feedback to an eNB.However, applications consistent with aspects herein can be made touplink (UL) traffic transmission with UL precoding (CoMP) wherein UE isforming beams based on feedback from the network (e.g., eNB). Thus, theroles of the UE and eNB change places with respect to increasingfeedback accuracy.

It should be appreciated with the benefit of the present disclosure thatMDC principles can be applied to any other type of feedback wherequantized quantity exhibits correlation in time and/or frequency. As aspecific example, we should use CQI reporting (which could be abroadband CQI and/or sub band specific CQI). Conventionally, a CQI wouldbe quantized with a fixed table wherein every value of the payload wouldmap to a certain C/I or rate (spectral efficiency) value. Instead, wecould use time-varying tables thereby achieving MDC gains in a lowmobility.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the embodiments disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, modules, circuits, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. Skilled artisans may implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present disclosure.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium may be integral to the processor.The processor and the storage medium may reside in an ASIC. The ASIC mayreside in a user terminal. In the alternative, the processor and thestorage medium may reside as discrete components in a user terminal.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentdisclosure. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the disclosure. Thus, the present disclosure is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

In view of the exemplary systems described supra, methodologies that maybe implemented in accordance with the disclosed subject matter have beendescribed with reference to several flow diagrams. While for purposes ofsimplicity of explanation, the methodologies are shown and described asa series of blocks, it is to be understood and appreciated that theclaimed subject matter is not limited by the order of the blocks, assome blocks may occur in different orders and/or concurrently with otherblocks from what is depicted and described herein. Moreover, not allillustrated blocks may be required to implement the methodologiesdescribed herein. Additionally, it should be further appreciated thatthe methodologies disclosed herein are capable of being stored on anarticle of manufacture to facilitate transporting and transferring suchmethodologies to computers. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media.

It should be appreciated that any patent, publication, or otherdisclosure material, in whole or in part, that is said to beincorporated by reference herein is incorporated herein only to theextent that the incorporated material does not conflict with existingdefinitions, statements, or other disclosure material set forth in thisdisclosure. As such, and to the extent necessary, the disclosure asexplicitly set forth herein supersedes any conflicting materialincorporated herein by reference. Any material, or portion thereof, thatis said to be incorporated by reference herein, but which conflicts withexisting definitions, statements, or other disclosure material set forthherein, will only be incorporated to the extent that no conflict arisesbetween that incorporated material and the existing disclosure material.

What is claimed is:
 1. A method for wirelessly receiving feedback,comprising: receiving a plurality of feedback reports; decoding multipledescription coding of the feedback reports over a plurality oftransmission intervals; determining a coherent channel across theplurality of transmission intervals; and combining the plurality offeedback reports for increased feedback accuracy for a coherent channelacross the transmission intervals.
 2. The method of claim 1, furthercomprising: detecting incoherence of the channel across a secondplurality of feedback reports; and determining channel state based uponindividual feedback reports for the second plurality of feedbackreports.
 3. The method of claim 1, further comprising decoding feedbackreports based upon a plurality of codebooks that correspond to and varywith respective transmission intervals.
 4. The method of claim 1,wherein the transmission intervals are time intervals.
 5. The method ofclaim 1, wherein the transmission intervals are frequency intervals. 6.The method of claim 1, wherein the transmission intervals are bothtime-based frames and frequency sub bands.
 7. The method of claim 1,further comprising receiving sequentially one of a plurality of bestcode representations from a single codebook in response to invariantfeedback.
 8. The method of claim 1, further comprising identifyingchannel direction information (CDI) feedback.
 9. The method of claim 1,further comprising combining multiple feedback reports by full channelfeedback in response to determining a coherent channel.
 10. The methodof claim 9, further comprising combining multiple feedback reports byoptimal combining through linear filtering of feedback corresponding todifferent instances with filter parameters selected consistent withmobility.
 11. The method of claim 10, further comprising linearfiltering by minimum means squared error.
 12. The method of claim 1,further comprising combining multiple feedback reports by heuristics forEigen-direction feedback.
 13. The method of claim 12, furthercomprising: choosing a weighting profile consistent with mobility; andobtaining a principal component of a non-negative Hermitian matrixcomputed as a weighted sum of outer auto-products of feedback receivedat different instances
 14. An non-transitory computer-readable storagemedium, comprising, a first set of codes for causing a computer toreceive a plurality of feedback reports; a second set of codes forcausing the computer to decode multiple description coding of thefeedback reports over a plurality of transmission intervals; a third setof codes for causing the computer to determine a coherent channel acrossthe plurality of transmission intervals; and a fourth set of codes forcausing the computer to combine the plurality of feedback reports forincreased feedback accuracy for a coherent channel across thetransmission intervals.
 15. An apparatus for wirelessly receivingfeedback, comprising: means for receiving a plurality of feedbackreports; means for decoding multiple description coding of the feedbackreports over a plurality of transmission intervals; means fordetermining a coherent channel across the plurality of transmissionintervals; and means for combining a plurality of feedback reports forincreased feedback accuracy for a coherent channel across transmissionintervals.
 16. An apparatus for wirelessly receiving feedback,comprising: a receiver for receiving a plurality of feedback reports;and a computing platform for decoding multiple description coding of thefeedback reports over a plurality of transmission intervals, fordetermining a coherent channel across the plurality of transmissionintervals, and for combining a plurality of feedback reports forincreased feedback accuracy for a coherent channel across transmissionintervals.
 17. The apparatus of claim 16, wherein the computing platformis further for detecting incoherence of the channel across a pluralityof feedback reports and for determining channel state based upon asingle feedback report.
 18. The apparatus of claim 16, wherein thecomputing platform is further for decoding feedback reports using aplurality of codebooks that correspond to and vary with respectivetransmission intervals.
 19. The apparatus of claim 16, wherein thetransmission intervals are time intervals.
 20. The apparatus of claim16, wherein the transmission intervals are frequency intervals.
 21. Theapparatus of claim 16, wherein the transmission intervals are bothtime-based frames and frequency sub bands.
 22. The apparatus of claim16, wherein the computing platform is further for decoding sequentiallyone of a plurality of best code representations from a single codebookin response to invariant feedback.
 23. The apparatus of claim 16,wherein the computing platform is further for identifying channeldirection information (CDI) feedback.
 24. The apparatus of claim 16,wherein the computing platform is further for combining multiplefeedback reports by full channel feedback in response to determining acoherent channel.
 25. The apparatus of claim 24, wherein the computingplatform is further for combining multiple feedback reports by optimalcombining through linear filtering of feedback corresponding todifferent instances with filter parameters selected consistent withmobility.
 26. The apparatus of claim 25, wherein the computing platformis further for linear filtering by minimum means squared error.
 27. Theapparatus of claim 26, wherein the computing platform is further forcombining multiple feedback reports by heuristics for Eigen-directionfeedback.
 28. The apparatus of claim 27, wherein the computing platformis further for estimating feedback by, choosing a weighting profileconsistent with mobility; and obtaining a principal component of anon-negative Hermitian matrix computed as a weighted sum of outerauto-products of feedback received at different instances